Infrared sensor control architecture

ABSTRACT

A system and method for optimizing fixed and temporal noise in an infrared imaging system. The system may use correction tables with correction factors, each correction factor indexed to a plurality of system parameters.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to copending U.S. non-provisionalapplication Ser. No. 12/762,113, filed on Apr. 16, 2010, which is anon-provisional of U.S. Provisional Application No. 61/259,079, filed onNov. 6, 2009, each of which is hereby incorporated by reference for allpurposes.

BACKGROUND

The present disclosure relates generally to solid state imaging sensors.In particular, to methods of enhancing image quality in infrared imagingsensors. Examples of methods of enhancing image quality in imagingsensors may be found in publications W02000052435A1 and W02003084213A1and in U.S. Patent Application Nos. 20020159101, 20020185601,20030183756 and 20030183765.

SUMMARY

Night vision sensors are widely used in military, security, andcommercial applications. The top-of-the line night vision systemsavailable today outperform the systems available just a few years agoand provide significant advantages to our military and security forces.

Existing sensors are configured to encode a wide dynamic range oftemperatures at the expense of sensitivity to small differences intemperature. This configuration exhibits good image contrast whenimaging objects with temperatures spread over the wide dynamic rangeavailable. Where scenes are cluttered with a number of objects ofsimilar temperature, the ability to detect and identify specific targetsis limited.

Other problems exist for infrared sensors as well. When tuned to performwell at a specific ambient temperature, changes in temperature such asnight passing to day may degrade performance and/or cause saturation inwarmer daytime hours. Similarly when tuned to a warmer temperature,cooler night time temperatures may result in low contrast images.

Improved calibration and optimization techniques can increasesensitivity, contrast and dynamic range for improved infrared sensorperformance and higher resolution images.

Configurations disclosed may include a method for operating an infraredimaging system with an array of pixels comprising developing correctiontables for the array of pixels including correction factors with offsetand gain values for each pixel. Also in the correction tables may be anindex of unique values for each correction table with an integrationtime value, a bucket fill value and a temperature value.

The method may further include acquiring at least one calibrating sceneimage with a selected integration time, determining bucket fill level ofthe at least one calibrating scene image, determining a scenetemperature of the at least one calibrating scene image, selecting acorrection table by indexing the selected integration time, determinedtemperature and determined bucket fill level to correction table indexvalues, applying the correction factors of the selected correction tableto the array of pixels and capturing a second image.

Also disclosed may be an infrared imaging system comprising a focalplane array with a plurality of pixels, a processor operable at least inpart to calculate a bucket fill level for the focal plane array, memoryincluding a set of non-uniform correction tables each table withcorrection factor values including a gain and an offset and index valuesunique to the table including a temperature, an integration time and abucket fill level; and means for determining the ambient temperature ofa scene where a non-uniform correction table is selected from memory andthe correction factor values applied to the plurality of pixels tominimize temporal and fixed noise levels.

Further disclosures may include a method for minimizing the noiseequivalent temperature difference value of a focal plane array withpixels in an infrared imaging system comprising defining a group ofbucket fill levels, defining a group of integration times and defining agroup of temperatures. The method may include creating pixel data bycreating an index by selecting one value for each of bucket fill level,integration time and temperature from each defined group, setting asubstantially blackbody to the selected temperature value of the index,setting the integration time of the imaging system to the selectedintegration time value of the index, setting the bucket fill level ofthe imaging system to the selected bucket fill level value of the index,with the blackbody image focused on the focal plane array, capturing oneor more data images; and saving to memory the pixel data of the dataimage indexed to the selected index values.

The method may further include repeating the creating step for eachunique set of index values, for each pixel calculating correctionfactors by solving a first linear equation of pixel data of a firstindex and a second linear equation of pixel data of a second index,storing the calculated correction factor in memory with the first indexas a correction factor index, acquiring an image of a scene at aselected integration time, determining scene temperature, calculating abucket fill level from one or more pixels selected from the acquiredimage, selecting correction factors from memory by correlatingcorrection factor index values to calculated bucket fill level,integration time and scene temperature and applying the selectedcorrection factors to pixels of the focal plane array.

An acquired image as defined here may correlate or correspond to a setof pixel values acquired from the focal plane array of pixels and storedin memory. A feature sub-region of the acquired image may be a subset ofpixels that correspond to a portion of or feature of the image. Forexample in an image with a human figure, a building and a background,the subset of pixels that show the human figure may be a featuresub-region separate from other pixels in the acquired image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of components and functions in an infraredimaging system including a focal plane array, scanning electronics,analog to digital converters and a processor.

FIG. 1A is a block diagram showing a correction table with correctionfactors and index that may be stored in memory.

FIG. 2 is a flow chart showing a standard method of calibration for animaging system.

FIG. 3 is a flow chart showing a multiple bucket fill method ofcalibration for an imaging system.

FIG. 4 is a flow chart showing a method for applying correction tablesof a multiple bucket fill calibration to an imaging system.

FIG. 5 is a flow chart showing a dynamic multiple integration/bucketfill method of calibration for an image system.

FIG. 6 is a representation of a set of correction tables with two valuesof an index.

FIG. 7 is a representation of a subset of correction tables with threeindex parameters.

FIG. 8 is a flow chart showing a method of calculating parameters for atarget bucket fill level.

FIG. 9 is a flow chart showing an iterative method of achieving a targetbucket fill level.

FIG. 10 is a flow chart showing a method if implementing a calibrationmethod with a limited number of correction tables.

FIG. 11 is an example of an image acquired by the imaging system with afeature sub-region.

FIG. 12 is a chart depicting standard calibration noise levels.

FIG. 13 is a chart depicting multiple bucket fill calibration noiselevels.

FIG. 14 is a chart depicting MIB calibration method noise levels.

FIG. 15 is a chart depicting standard calibration and MIB calibrationnoise at temperature.

DETAILED DESCRIPTION

Low light imaging sensors can be implemented using various technologies.Sensors may incorporate bolometers to detect a heat related change inresistance in an element where a photon impinging on the element raisesthe temperature of the element.

A more common type of detector is a solid state element that accumulatesa charge from absorbed photons. FIG. 1 is a block diagram representationof an infrared imaging system 10 including a focal plane array or FPA 12with pixels 12A shown as a dotted line box, an FPA cooler 14, opticalfocusing system 16, array scanning electronics 18, analog to digitalconverter 20, memory or registers 22, processor 24, gain voltage source26 and display 28. Infrared imaging system 10 may include or may be usedin association with a substantially blackbody or calibration paddle 29connected to a temperature control 29A.

FIG. 1A shows memory 22 of FIG. 1 with a non-uniform correction (NUC) orcorrection table 30 that may include correction factors or correctionfactor set 32 for focal plane array 12 and/or pixels 12A as well asindex or index values 34.

Focal plane array 12 of discrete pixels 12A may be exposed to a scenewith features at different temperatures. The scene may have limitedambient visible light and the primary source of photons may be in aninfrared range from the ambient heat of various features in the scene.In a night scene the features with the most ambient heat may be mammals,buildings and objects with residual heat absorbed from sunlight. Theremay be a wide range of temperatures due to shading, internal heating,etc.

The infrared light from the scene may be focused by optics 16 or otherphysical arrangement to create an image at FPA 12. Each pixel 12A maycharge at a rate related to the number of photons hitting it. Each pixelwill charge to a value between zero and some maximum voltage value.Light is collected over a set period called an integration time and atthe end of the period each pixel element is polled and its charge levelis converted to a digital value at analog to digital converter 20 andstored in memory 22. These digital values may then be used to create arecorded image which may be presented on display 28.

Image saturation occurs when by the end of the integration time manypixels reach their maximum charge so that scene details are notcaptured. A gain factor can be applied to pixels 12A by gain voltage 26to act as a multiplier for the rate of charge with each impingingphoton. The gain can be set so that a very hot scene with many impingingphotons doesn't charge a majority of pixels to their maximum value in avery short period.

At the same time, a higher charge per pixel or bucket fill at a levelbelow saturation level gives a higher signal to noise ratio. Bucket fillrelates to the percentage fill of the digital registers read by theanalog-to-digital converter. This may correspond to the average chargeof all pixels in the focal plane array as a proportion of maximum chargefor the pixels.

Two measures of acquired image quality are fixed pattern noise andtemporal noise. Fixed pattern noise is induced by the inherent responsevariations between pixels, for example the variation in rate of chargeof a pixel for an identical number of photons. Temporal noise comes fromall time-varying noise sources such as readout electronics andstatistics of underlying Poisson processes. Minimizing the total noisefrom these sources in relation to signal improves image quality.

Three parameters that affect the signal to noise ratio are pixel offset,pixel gain and integration time. Setting these parameters to optimumvalues for the current conditions can greatly improve image quality.This is typically done by calibrating some settings at the factory thatare stored in non-uniform correction (NUC) table 30 in memory 22. Thesevalues can be referenced in the field and applied to pixels 12A of focalplane array 12. Other parameters are set in the field using atemperature reference to develop additional pixel correction tables 30based on current conditions. These corrections adjust pixel gain andoffset to optimize the signal to noise ratio for current conditions.

Offset values for the NUC table 30 may be determined in the field with ascene of one uniform temperature, usually a paddle configured as asubstantially blackbody 29 and held at a fixed temperature across theentire face. In practice blackbody 29 may be as simple as a piece offoam or metal at room temperature. An image of blackbody 29 is focusedon focal plane array 12. Pixel response may be measured with a fixedintegration time at each temperature and correction factor 32 may berecorded in NUC table 30.

FIG. 2 is a flow chart of steps used in a standard calibration 50 of aninfrared imaging system 10. In step 52 the integration time is set. Instep 54 the calibration of the blackbody is set to temperature. In step56 one or more images is acquired for blackbody 29 and pixel data issaved in memory 22. In step 58, if all values of temperature have beenused, control goes to step 60, else control returns to step 54 and anext temperature is set. In step 60 gain correction factor values foreach pixel are derived based on at least two sets of pixel data andstored in correction table 30 with an associated index 34. These stepsare grouped by a dotted line box 50A and may be performed at the factorybefore delivery of imaging system 10. In step 62 an offset correctionfactor may be determined by acquiring one or more images of blackbody 29at a current scene temperature. In step 64 correction factors areapplied to pixels 12A. In step 66 an image is acquired.

The steps of FIG. 2 may be executed in a different order where itachieves similar results. For example, deriving correction factors ofstep 60 may be done at least in part between the iterative steps ofsetting temperature for blackbody 29.

This standard calibration is commonly-called a “two-point” or “n-point”correction method. Two uniform sources of known intensity, such asblackbody 29 at two (or more) different temperatures, are sequentiallyimaged as in step 54. Each pixel response can be approximated as ameasured variable y(t) and a real-world variable x(t) can be written as:

y(t)=a*x(t)+b

with a and b being the pixel's multiplicative gain and additive offset,respectively. By focusing the image of blackbody 29 on focal plane array12 at two temperatures and recording the pixel values for each image, alinear system is created with two or more equations and two unknowns ateach pixel. The gain and offset values or correction factor 32 for eachpixel can be easily computed and stored as part of a correction table30. These correction factor derivation methods are common and well knownto those skilled in the art.

The histogram equalization of step 64 is a mathematical transformtypically used for images with low contrast, either with background andforeground that are low intensity or both high intensity. In a lowintensity image the number of pixel level occurrences on a grayscale areat a low end. Equalization resets pixel values so that the occurrencesare spread more evenly across the grayscale. A high intensity image haspixel level occurrences at the high end of the grayscale. Equalizationsimilarly resets pixel values to spread them more evenly across thegrayscale while maintaining image characteristics.

Noise Equivalent Temperature Difference (NETD) is a standard measure ofperformance for sensors as may be used in an infrared camera. Fixedpattern noise is calculated by computing the average of eachNUC-corrected pixel using collected images of a blackbody source at aparticular temperature and sensor integration time. This set of averagedpixels is known as the fixed pattern noise (FPN) image. Fixed patternnoise—the deviation from the mean over all pixels—is then estimated asthe standard deviation of all pixel samples. Dividing this value by thesystem gain at this integration time yields the corresponding NETD andis expressed in degrees Kelvin. Chart 1 shown in FIG. 12 is an exampleof noise in an imaging system calibrated by standard calibration method50.

Standard calibration method 50 may result in low fixed pattern noise forthe one integration time at which the offset correction was calculatedand at scene temperatures that are close to the temperature of thepaddle 29. But if the integration time is held fixed, temporal noisewill be high for scenes with low bucket fill or saturation may occur inhigh temperature scenes. Varying the integration time withoutre-calculating the offset correction results in high fixed patternnoise. Also scene temperatures far from the “paddle” temperature showincreased fixed pattern noise.

As an example, a flight crew may prepare at dusk for a night timereconnaissance mission. They execute a “paddle” calibration of thecamera before take-off in the hangar at 1 OC with the integration timeset to 5 ms to prevent image saturation. Initial imagery looks good, butas their northern latitude location cools, noise relative to signalgrows. By the time the ground reaches −40 C, the noise swamps most ofthe signal. The signal could be increased by increasing the integrationtime, but changing the integration time from the one at which the“paddle” calibration was done will result further increase fixed patternnoise.

An improved system would automatically adjust internal camera gain,offset and integration time to continuously optimize output image signalto noise while maintaining dynamic range sufficient to preventsaturation. The system should operate without operator intervention andwithout calibration paddles. The system should have a response timecapable of handling both slow and fast changes in backgroundtemperatures to accommodate both gradual ambient temperature shifts andrapid scene changes. It should also store or automatically computecorrection factors 32 to match the current system operating parameters.The system should maintain a high bucket fill, which optimizes signal tonoise over a wide range of scene temperatures.

Multiple Bucket Fill Calibration Method

FIG. 3 is a flow chart of a multiple bucket fill calibration method 100using correction factors 32 associated with index 34. In step 102 anintegration time, a set of temperatures and a set of bucket fill levelsare selected as index values 34. Using a selected index 34 with a uniqueset of values and at step 104 the integration time of the selected index34 is configured. In step 106 the bucket fill level is set and blackbody29 is configured to the temperature of selected index 34 in step 108. Instep 110 pixel data of one or more images of blackbody 29 is acquiredand added to memory 22.

At step 112 if all fill levels from the set of bucket fill levels havebeen sampled, control goes to step 114. If all fill levels have not beensampled, control returns to step 108 and a new fill level is set untilall fill levels have been completed. When all fill levels have beensampled control then goes to step 116 where correction factors arederived from first and second pixel data and correction factors 32 areadded to correction table 30 with index values 34. At step 118calibration ends. Each bucket fill level used and each temperature usedare new unique indexes 34. This method produces correction factors 32and correction tables 30 at multiple parameter set points. First andsecond pixel data for deriving correction factors is typically two setsof data with index values 34 adjacent or proximate in value.

Chart 2 shown in FIG. 13 is an example of noise levels for imagingsystem 10 after multiple bucket fill calibration 100. In this example 17bucket fill levels are used and three temperatures. As shown in Chart 2in FIG. 13, multiple bucket fill method 100 offers a substantialimprovement over standard calibration method 50. Levels of temporal andfixed pattern noise are noticeably lower than the levels resulting fromstandard calibration method 50. This illustrates the observation thatalthough overall detector performance remains approximately linear,gains and offset changes need to be adjusted at different fill levels.

FIG. 4 is a flow chart illustrating an implementation method 150 forapplying correction factors 32 during operation of imaging system 10. Instep 152 a scene image is acquired at focal plane array 12. At step 154the average bucket fill of the scene image is calculated from pixels ofthe acquired image of step 152. Correction table 30 with index valuescorrelating to the scene image is selected at step 156. Gain and offsetvalues from the selected table are applied to pixels 12A of imagingsystem 10 at step 158. A second calibrated scene image is acquired atstep 160.

In an alternate configuration, step 152 may be repeated one or moretimes and the average bucket fill may be determined from the multipleacquired scene images. In another alternate configuration a subset ofthe pixels of the acquired image of step 152 may be used to calculatethe average bucket fill in step 154. The subset of pixels from theacquired image of step 152 may be a region of interest such as a featurein the image.

[36] In yet another alternate configuration step 152 may be repeated toacquire multiple images and a subset of pixels from each of the multipleacquired scene images of step 152 may be used in calculating the averagebucket fill of step 154. The subset of pixels may be as small as onepixel and the average bucket fill may be determined by selecting acorrelated pixel from each of several scenes. The pixel selected may bethe same pixel in focal plane array 12 for each image or the pixel maybe a specific pixel related to the scene such as a corner of a buildingwhich in a moving scene may correlate to different pixels of focal planearray 12 in successive images.

The subset of pixels or the one or more pixels of the acquired imageused to determine bucket fill may be selected to optimize theconfiguration of imaging system 10 to show a specific feature. Forexample a feature sub-region of the acquired image may show a building.One or more pixels of the feature sub-region may be selected fordetermining a bucket fill level in order to optimize the acquisition ofadditional images of the building.

Multiple Integration Time and Bucket Fill (MIB) Calibration Method

FIG. 5 is a flow chart similar to multiple bucket fill calibrationmethod 100 of FIG. 4 but with an additional parameter of integrationtime to create a multiple integration time and bucket fill (MIB)calibration method 200. In step 202 an integration time, a set oftemperatures and a set of bucket fill levels are selected as values forindex 34. In step 204 an integration time is set. In step 206 thetemperature of a blackbody is set and a bucket fill level is set. Instep 208 one or more blackbody images is acquired and pixel data isstored in memory 22. At step 210 and 212 if all values of bucket fill,integration times and temperatures have not been sampled, controlreturns to step 204 and step 206 to set new integration times and bucketfill levels until all values have been tested. Each iterationcorresponds to a new index 34 with unique values. Control then goes tostep 214 where correction factors are derived from first and secondpixel data. The correction factors 32 and index values are stored inmemory 22 as correction tables 30. MIB calibration method 200 ends at214. This may produce correction factors 32 at multiple parameter setpoints including a range of integration times.

Chart 3 shown in FIG. 14 is an example of noise levels for imagingsystem 10 after MIB calibration 200. Performance is significantlyimproved over both standard calibration method 50 and the multiplebucket fill method 200. Fixed pattern noise is held to a low, nearlyconstant level well below the level of temporal noise.

While these examples are for a set range of relatively hightemperatures, similar results may be duplicated at lower temperatures.Correction tables 30 developed in the method of FIG. 5 may beimplemented and applied in a similar method as described in FIG. 4.

FIGS. 6 and 7 are examples of NUC or correction tables 30. FIG. 6 showsa set of NUC or correction tables 30 for four pixels of a focal planearray 12. This is obviously greatly simplified. A focal plane array canhave pixels numbering in the millions.

Here there are two correction factors 32 and two parameters of indexes34. Each correction table has a unique set of index values. The indexvalues for the correction table may be part of index 34. The indexparameters here are bucket fill and temperature. The index values of thefirst row are 70 and 10 in correction table 302 a. The index values inthe second row are 70 and 60 in correction table 302 b, etc. This hasresulted in four correction tables 30.

FIG. 7 is a subset of four correction tables 352 a, b, c and d similarto those in FIG. 6. Here a third index parameter of integration time isincluded as may be the case with MIB calibration method 200. The fourcorrection tables shown in the example are for one integration time of aset of integration times. If four set points or values are used for theintegration time parameter, twelve correction tables will be produced.Index 34 for table 352 a has values of 40, 10 and 5.

The use of tables in these examples is for the purposes of example only.Values may be stored in memory as a series of pixel locations on thefocal plane array with a list of associated values. Tables may be amnemonic for understanding how the values are used.

The set of Index values 34 may be unique and each table will have it'sown unique set of index values as well as correction factors 32 for allpixels 12A in focal plane array 12. An image acquired by imaging system10 may have a set of characteristic values that may include one or morefrom the value set of bucket fill value, scene temperature andintegration time. Applying the correction factors 32 of correctiontables 30 may include correlating or indexing the characteristic valuesof the acquired image to index values 34 of correction table 30.Indexing or correlating may include matching the temperature values, theintegration times and/or the bucket fill values between the imagecharacteristic values and the index values 34 to identify a closestagreement.

The parameters and values used here for characteristic values areexamples. Other sets of parameters and values which perform a similarfunction tying correction factors 32 to pixels may be used and stillfall within the scope of this disclosure.

Applying the correction factors 32 to pixels of the focal plane arraymay be done by any of a number of methods which are well known topersons with ordinary skill in the art. The correction factors 32 may beapplied so that it affects the actual function of the individual pixelsin acquiring charge. Correction factors 32 may be applied to thedigitized values of the pixels stored in memory 22. Any method ofapplying correction factors 32 from correction tables 30 that reducesthe noise level in an acquired image will fall within the scope of thisdisclosure.

Dynamic MIB Calibration Method

A disadvantage of MIB calibration method 200 is that it createsexponentially more correction tables 30 and requires more memory andlookup time. Imaging system 10 may be configured to operate within alimited bucket fill range using a target bucket fill 36.

FIG. 8 is a flow chart describing a method 400 for selecting anintegration time that produces target bucket fill 36 and uses a limitednumber of correction tables 30. In step 402 first and second integrationtimes and a target bucket fill 36 are selected. In step 404 a firstimage is captured at the first integration time. In step 406 a secondimage is captured of the same scene. In step 408 the scene integrationtime for bucket fill level 36 can be derived as a two equation linearsystem with integration time and bucket fill factors in a similar manneras described above for deriving correction factors 32. In step 410 theintegration time for imaging system 10 is set.

This method is often sufficient because the non-uniformity and temporalnoise curves are smooth and do not vary over large magnitudes. Also,processing the corrected image with histogram equalization or similaralgorithm results in an output image that maintains a fairly consistentintensity even as the integration time changes to a level short ofsaturation. Only the noise level will change in a fairly static scene.

Target bucket fill 36 may have an assigned value, but may be implementedas a range of values. Target bucket fill 36 may have a toleranceresulting in a range of allowed bucket fill values around target bucketfill 36. This may be a function of limitations of imaging system 10 ormay be a function of limitations of the calculation accuracy or otherfactors.

Method 400 may require a substantially static scene. In a moving scene,the two sample technique may result in an unstable solution that bouncesbetween too bright and too dark. As most systems have a limited range ofintegration times, such as 1 to 20 milliseconds in 1 millisecond steps,it's feasible to iteratively set an integration time, measure the bucketfill of a captured image and adjust the integration time appropriatelyuntil the desired bucket fill level is achieved. This may be useful fornon-static scenes.

FIG. 9 is a flow chart illustrating a variation of method 400 withiterative steps 450 for configuring imaging system 10 to target bucketfill 36. In step 452 a first integration time is set. This may initiallybe a default setting. At step 454 an image is acquired. At step 456 thebucket fill level for the acquired image is determined. At step 458 thedetermined bucket fill level is compared to target bucket fill level 36.If the determined bucket fill level is high, integration time may bedecremented at step 460. If in step 458 the determined bucket fill levelis low, the integration time may be incremented at step 462. In bothcases, control may return to step 454 where a new image is acquired. Ifat step 458 the acquired image is at target bucket fill level 36 controlmay pass to step 464 and system integration time may be set to thecurrent integration time. At step 466 an image may be acquired at thesystem integration time and operation of imaging system 10 continues.

FIG. 10 is a flow chart of a dynamic method 500 of implementing the MIBcalibration method 200 with imaging system 10 configured to targetbucket fill 36. In step 502 target bucket fill level 36 is assigned toimaging system 10. In step 504 a set of correction tables 30 aredeveloped for imaging system 10 that are indexed to a range ofintegration times, a range of temperatures and the assigned targetbucket fill level 36. Imaging system 10 identifies an appropriateintegration time to attain the assigned bucket fill level in step 506.In step 508 a scene temperature is measured. In step 500 a correctiontable is selected by matching the imaging system integration time andmeasured scene temperature to the indexes of the correction table. Instep 512 the correction factors 32 are applied to the pixels 12A offocal plane array 12.

By operating at a target bucket fill 36 fewer correction factors 32 needto be stored by the system, memory requirements are lower and lookuptimes can be greatly reduced. Also a bucket fill range can be selectedthat minimizes the combined noise and optimizes image quality. A noisecomparison of the Standard Method and our dynamic MIB calibration method500 is shown in Chart 4 in FIG. 15. They axis scale of chart 4 ischanged from the y axis scales of previous charts.

In chart 4 in FIG. 15 the dynamic MIB calibration method is shown tomaintain a much lower noise level across all temperatures in thisexample. This method also does not require a blackbody paddle 29 and theassociated temperature maintenance equipment required for the standardmethod.

Continuing the previous field example, in the standard method, theflight crew executes a “paddle” calibration on their imaging system at60 C with an integration time of 5 ms. As the temperature is dropped at5 C steps down to 10 C, the temporal noise is fairly constant, but thefixed pattern noise climbs significantly, resulting in poor lowtemperature performance.

Using dynamic MIB calibration method 500, with target bucket fill 36 of60%, the flight crew does not need to execute any “paddle” calibrations.Imaging system 10 automatically adjusts the integration time and usescoefficient factors which are matched to the scene temperature andintegration time. At all temperature levels, there is reduced temporalnoise due to the longer integration time that the system settles on.There is also low and relatively flat fixed pattern noise due to the NUCcorrection being optimized for the current scene temperature. OverallNETD is reduced by greater than a factor of 5 at the lowest temperature.

A parallel and similar method is to select a target integration time forimaging system 10 rather than a bucket fill level. Then a set ofcorrection tables 30 are developed based on a target integration time.Correction table 30 is then selected in operation by the current bucketfill level and the scene temperature.

FIG. 6 is an example of an acquired image 600 as may be captured atfocal array 12 by image system 10. Image 600 in this example includescars 602, trees 604 and pedestrians or people 606 as seen from above.Image 600 may be shown on display 28 and may correlate or correspond topixel values at focal plane array 12. The pixel values may be stored inmemory 22. One person of people 606 in image 600 is denoted by a dottedline. The subset of pixels that comprise the portion of image 600 withinthe dotted line may comprise a feature sub-region 608 of image 600. Oneor more pixels comprising feature sub-region 608 may be selected andused to determine a bucket fill value for image 600. Feature sub-region608 may be used to determine a bucket fill value to optimize imagingsystem 10 for depicting the feature in later images.

Where multiple images are acquired and used to calculate a single bucketfill value, a feature sub-region 608 may be defined for each image 600.The one or more pixels comprising each feature sub-region 608 may differin successive images as the position of the feature of interest occupiesdifferent xy coordinates in successive images.

The example shown of a person as subject of a feature sub-region is anexample. Any feature sub-region, subset of pixels or all of the pixelsmay be used in determining a bucket fill value for an image. The subsetof pixels selected may define a feature sub-region or may not.

This invention is not limited by a method of implementation. Calibrationmethods may be applied at the pixel. The methods may be implementedusing a custom integrated circuit or programmable IC. The methods may beimplemented through software algorithms that set camera parameters forimage acquisition and/or manipulate image pixel values stored in memory.The calibration methods may use a hybrid of implementation methods.

These configurations and methods shown are examples for the purpose ofillustration and are not to be used as limitations. Any combination ofsteps or components which perform a similar function falls within thescope of this disclosure. Any suitable configuration or combination ofcomponents presented, or equivalents to them that perform a similarfunction, may also be used. And while infrared imaging systems have beendiscussed and used in the examples, this method and system is applicableto any image analysis for any wavelength.

While embodiments of an image analysis system and methods of use havebeen particularly shown and described, many variations may be madetherein. This disclosure may include one or more independent orinterdependent inventions directed to various combinations of features,functions, elements and/or properties, one or more of which may bedefined in the following claims. Other combinations and sub-combinationsof features, functions, elements and/or properties may be claimed laterin this or a related application. Such variations, whether they aredirected to different combinations or directed to the same combinations,whether different, broader, narrower or equal in scope, are alsoregarded as included within the subject matter of the presentdisclosure. An appreciation of the availability or significance ofclaims not presently claimed may not be presently realized. Accordingly,the foregoing embodiments are illustrative, and no single feature orelement, or combination thereof, is essential to all possiblecombinations that may be claimed in this or a later application. Eachclaim defines an invention disclosed in the foregoing disclosure, butany one claim does not necessarily encompass all features orcombinations that maybe claimed. Where the claims recite “a” or “afirst” element or the equivalent thereof, such claims include one ormore such elements, neither requiring nor excluding two or more suchelements. Further, ordinal indicators, such as first, second or third,for identified elements are used to distinguish between the elements,and do not indicate a required or limited number of such elements, anddo not indicate a particular position or order of such elements unlessotherwise specifically stated.

1. A method for operating an infrared imaging system with an array ofpixels comprising: developing correction tables for the array of pixelsincluding: correction factors with offset and gain values for eachpixel; and an index of unique values for each correction table with: anintegration time value; a bucket fill value; and a temperature value;acquiring at least one calibrating scene image with a selectedintegration time; determining bucket fill level of the at least onecalibrating scene image; determining a scene temperature of the at leastone calibrating scene image; selecting a correction table by indexingthe selected integration time, determined temperature and determinedbucket fill level to correction table index values; applying thecorrection factors of the selected correction table to the array ofpixels; and capturing a second image.
 2. The method for operating aninfrared imaging system of claim 1 where developing a correction tableincludes defining values for bucket fill levels, temperature andintegration time to be incorporated in indexes.
 3. The method foroperating an infrared imaging system of claim 2 where developing acorrection table includes configuring the imaging system to each uniqueset of index values and capturing an image.
 4. The method for operatingan infrared imaging system of claim 1 where capturing a second imageincludes operating the imaging system to maintain a target bucket filllevel.
 5. The method for operating an infrared imaging system of claim 4where developing correction tables includes substantially limiting thebucket fill value of the index to the target bucket fill level.
 6. Themethod for operating an infrared imaging system of claim 4 whereoperating the imaging system to maintain a target bucket fill levelincludes iteratively: capturing one or more images; determining thedifference between the target till bucket level and the calculatedbucket fill; calculating the bucket fill for each of the one or moreimages; and selecting an integration time based on the calculated bucketfill.
 7. The method for operating an infrared imaging system of claim 1where capturing a second image includes applying a histogramequalization algorithm to the captured image.
 8. A method for minimizinga noise equivalent temperature difference value of a focal plane arraywith pixels in an infrared imaging system comprising: defining a groupof bucket fill levels; defining a group of integration times; defining agroup of temperatures; creating pixel data by creating an index byselecting one value for each of bucket fill level, integration time andtemperature from each defined group; setting a substantially blackbodyto the selected temperature value of the index; setting the integrationtime of the imaging system to the selected integration time value of theindex; setting the bucket fill level of the imaging system to theselected bucket fill level value of the index; with the blackbody imagefocused on the focal plane array, capturing one or more data images; andsaving to memory the pixel data of the data image indexed to theselected index values; repeating the creating step for each unique setof index values; for each pixel calculating correction factors bysolving: a first linear equation of pixel data of a first index; and asecond linear equation of pixel data of a second index; storing thecalculated correction factor in memory with the first index as acorrection factor index; acquiring an image of a scene at a selectedintegration time; determining scene temperature; calculating a bucketfill level from one or more pixels selected from the acquired image;selecting correction factors from memory by correlating correctionfactor index values to calculated bucket fill level, integration timeand scene temperature; and applying the selected correction factors topixels of the focal plane array.
 9. The method for minimizing the noiseequivalent temperature difference of a focal plane array of claim 8where the group of bucket fill levels has one value.
 10. The method forminimizing the noise equivalent temperature difference of a focal planearray of claim 8 where the infrared imaging system is configured toacquire images at one target bucket fill value.
 11. The method forminimizing the noise equivalent temperature difference of a focal planearray of claim 10 where configuring the imaging system to operate at onebucket fill value includes iteratively acquiring a scene image,calculating the bucket fill level of the acquired scene image, resettingintegration time and acquiring a scene image.
 12. The method forminimizing the noise equivalent temperature difference of a focal planearray of claim 8 where a histogram equalization algorithm is applied tothe acquired image.
 13. The method for minimizing the noise equivalenttemperature difference of a focal plane array of claim 8 where the oneor more pixels selected from the acquired image are selected from afeature sub-region of the acquired image.